1 - Create Two Scenarios with Two Materials¶

This journal shows how to load the baselines and run the dynamic mas flow analysis, plotting the results for two scenarios and two materials.

Step 1: Set Working Folder and Import PV ICE¶

In [1]:
import os
from pathlib import Path

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')

# Another option using relative address; for some operative systems you might need '/' instead of '\'
# testfolder = os.path.abspath(r'..\..\PV_DEMICE\TEMP')  

print ("Your simulation will be stored in %s" % testfolder)

Your simulation will be stored in C:\Users\sayala\Documents\GitHub\PV_ICE\PV_ICE\TEMP
In [2]:
import PV_ICE

Step 2: Add Scenarios and Materials¶

silicon and glass materials are added to the two simulations, along with a scenario, in this case the baseline_modules_US. The baseline files for decadence scenario will be modified.

In [3]:
r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder)
r1.createScenario(name='standard', file=r'..\baselines\baseline_modules_US.csv')
r1.scenario['standard'].addMaterial('glass', file=r'..\baselines\baseline_material_glass.csv')
r1.scenario['standard'].addMaterial('silicon', file=r'..\baselines\baseline_material_silicon.csv' )

r1.createScenario('decadence', file=r'..\baselines\baseline_modules_US.csv')
r1.scenario['decadence'].addMaterial('glass', file=r'..\baselines\baseline_material_glass.csv')
r1.scenario['decadence'].addMaterial('silicon', file=r'..\baselines\baseline_material_silicon.csv')

path = C:\Users\sayala\Documents\GitHub\PV_ICE\PV_ICE\TEMP

Step 3: Modify Parameters in the Scenarios¶

We have some functions to create changes based on improvements, but for this example we'll just be modifying values for the full column and comparing effects.

In [4]:
r1.scenario['decadence'].data['mod_lifetime'] = 35
r1.scenario['decadence'].material['glass'].materialdata['mat_virgin_eff'] = 70.0

r1.scenario['decadence'].material['silicon'].materialdata['mat_virgin_eff'] = 80.0
r1.scenario['decadence'].material['silicon'].materialdata['mat_EOL_collected_Recycled'] = 100.0
r1.scenario['decadence'].material['silicon'].materialdata['mat_massperm2'] = 22

Step 4: Run the Mass Flow Calculations on All Scenarios and Materials¶

In [5]:
r1.calculateMassFlow()
r1.aggregateResults();

Working on Scenario:  standard
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
Working on Scenario:  decadence
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon

Step 5: Use internal plotting functions to plot results¶

Pull out the keywords by printing the keys to the module data or the material data:

print(r1.scenario.keys())

print(r1.scenario['standard'].data.keys())

print(r1.scenario['standard'].material['glass'].materialdata.keys())
In [6]:
#print(r1.scenario.keys())
#print(r1.scenario['standard'].data.keys())
print(r1.scenario['standard'].material['glass'].materialdata.keys())

Index(['year', 'mat_virgin_eff', 'mat_massperm2', 'mat_MFG_eff',
       'mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff',
       'mat_MFG_scrap_Recycled_into_HQ',
       'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG',
       'mat_EOL_collected_Recycled', 'mat_EOL_Recycling_eff',
       'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG',
       'mat_modules_Collected', 'mat_modules_NotCollected',
       'mat_modules_Recycled', 'mat_modules_NotRecycled',
       'mat_EOL_sento_Recycling', 'mat_EOL_NotRecycled_Landfilled',
       'mat_EOL_Recycled', 'mat_EOL_Recycled_Losses_Landfilled',
       'mat_EOL_Recycled_2_HQ', 'mat_EOL_Recycled_2_OQ',
       'mat_EoL_Recycled_HQ_into_MFG', 'mat_EOL_Recycled_HQ_into_OU',
       'mat_UsedSuccessfullyinModuleManufacturing',
       'mat_EnteringModuleManufacturing', 'mat_LostinModuleManufacturing',
       'mat_Manufacturing_Input', 'mat_MFG_Scrap',
       'mat_MFG_Scrap_Sentto_Recycling', 'mat_MFG_Scrap_Landfilled',
       'mat_MFG_Scrap_Recycled_Successfully',
       'mat_MFG_Scrap_Recycled_Losses_Landfilled', 'mat_MFG_Recycled_into_HQ',
       'mat_MFG_Recycled_into_OQ', 'mat_MFG_Recycled_HQ_into_MFG',
       'mat_MFG_Recycled_HQ_into_OU', 'mat_Virgin_Stock',
       'mat_Virgin_Stock_Raw', 'mat_Total_EOL_Landfilled',
       'mat_Total_MFG_Landfilled', 'mat_Total_Landfilled',
       'mat_Total_Recycled_OU'],
      dtype='object')
In [7]:
r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure')
In [8]:
r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled')
In [9]:
r1.plotMaterialResults(keyword='VirginStock')
In [10]:
r1.plotMetricResults()

['standard', 'decadence']
In [11]:
r1.plotInstalledCapacityResults()

['standard', 'decadence']
In [12]:
datay = r1.USyearly
datac = r1.UScum
In [13]:
        filter_colc = [col for col in datac if col.startswith('newInstalledCapacity')]
        filter_coly = [col for col in datay if col.startswith('Capacity')]
In [14]:
datac[filter_colc]
Out[14]:
newInstalledCapacity_standard_[MW] newInstalledCapacity_decadence_[MW]
year
1995 1.250000e+01 1.250000e+01
1996 2.216767e+01 2.216767e+01
1997 3.378967e+01 3.378967e+01
1998 4.557067e+01 4.557067e+01
1999 6.245533e+01 6.245533e+01
2000 8.338200e+01 8.338200e+01
2001 1.113670e+02 1.113670e+02
2002 1.538430e+02 1.538430e+02
2003 2.135880e+02 2.135880e+02
2004 3.083400e+02 3.083400e+02
2005 4.049212e+02 4.049212e+02
2006 5.031342e+02 5.031342e+02
2007 8.273670e+02 8.273670e+02
2008 1.141952e+03 1.141952e+03
2009 1.573197e+03 1.573197e+03
2010 2.293041e+03 2.293041e+03
2011 4.074418e+03 4.074418e+03
2012 7.138581e+03 7.138581e+03
2013 1.117603e+04 1.117603e+04
2014 1.489823e+04 1.489823e+04
2015 2.173256e+04 2.173256e+04
2016 3.463022e+04 3.463022e+04
2017 4.441473e+04 4.441473e+04
2018 5.376726e+04 5.376726e+04
2019 6.471704e+04 6.471704e+04
2020 7.962083e+04 7.962083e+04
2021 9.607639e+04 9.607639e+04
2022 1.113036e+05 1.113036e+05
2023 1.265276e+05 1.265276e+05
2024 1.431621e+05 1.431621e+05
2025 1.612770e+05 1.612770e+05
2026 1.810041e+05 1.810041e+05
2027 2.024869e+05 2.024869e+05
2028 2.258817e+05 2.258817e+05
2029 2.513587e+05 2.513587e+05
2030 2.791031e+05 2.791031e+05
2031 3.093167e+05 3.093167e+05
2032 3.422194e+05 3.422194e+05
2033 3.780504e+05 3.780504e+05
2034 4.170703e+05 4.170703e+05
2035 4.595630e+05 4.595630e+05
2036 5.058376e+05 5.058376e+05
2037 5.562306e+05 5.562306e+05
2038 6.111086e+05 6.111086e+05
2039 6.708708e+05 6.708708e+05
2040 7.359517e+05 7.359517e+05
2041 8.068249e+05 8.068249e+05
2042 8.840058e+05 8.840058e+05
2043 9.680558e+05 9.680558e+05
2044 1.059586e+06 1.059586e+06
2045 1.159263e+06 1.159263e+06
2046 1.267811e+06 1.267811e+06
2047 1.386019e+06 1.386019e+06
2048 1.514749e+06 1.514749e+06
2049 1.654935e+06 1.654935e+06
2050 1.807597e+06 1.807597e+06
In [15]:
data = datac[filter_colc].copy()
data.join(datay[filter_coly].copy())
Out[15]:
newInstalledCapacity_standard_[MW] newInstalledCapacity_decadence_[MW] Capacity_standard_[MW] Capacity_decadence_[MW]
year
1995 1.250000e+01 1.250000e+01 1.250000e+01 1.250000e+01
1996 2.216767e+01 2.216767e+01 2.209888e+01 2.209888e+01
1997 3.378967e+01 3.378967e+01 3.359842e+01 3.359842e+01
1998 4.557067e+01 4.557067e+01 4.518868e+01 4.518868e+01
1999 6.245533e+01 6.245533e+01 6.180214e+01 6.180214e+01
2000 8.338200e+01 8.338200e+01 8.232501e+01 8.232501e+01
2001 1.113670e+02 1.113670e+02 1.096988e+02 1.096988e+02
2002 1.538430e+02 1.538430e+02 1.512488e+02 1.512488e+02
2003 2.135880e+02 2.135880e+02 2.095747e+02 2.095747e+02
2004 3.083400e+02 3.083400e+02 3.021966e+02 3.021966e+02
2005 4.049212e+02 4.049212e+02 3.859873e+02 3.956005e+02
2006 5.031342e+02 5.031342e+02 4.736559e+02 4.894643e+02
2007 8.273670e+02 8.273670e+02 7.947645e+02 8.080952e+02
2008 1.141952e+03 1.141952e+03 1.103995e+03 1.114463e+03
2009 1.573197e+03 1.573197e+03 1.527483e+03 1.534977e+03
2010 2.293041e+03 2.293041e+03 2.236089e+03 2.240861e+03
2011 4.074418e+03 4.074418e+03 4.002599e+03 4.005224e+03
2012 7.138581e+03 7.138581e+03 7.037122e+03 7.045450e+03
2013 1.117603e+04 1.117603e+04 1.103376e+04 1.104702e+04
2014 1.489823e+04 1.489823e+04 1.469685e+04 1.471694e+04
2015 2.173256e+04 2.173256e+04 2.146720e+04 2.148175e+04
2016 3.463022e+04 3.463022e+04 3.427216e+04 3.428151e+04
2017 4.441473e+04 4.441473e+04 4.391453e+04 4.391967e+04
2018 5.376726e+04 5.376726e+04 5.308262e+04 5.308494e+04
2019 6.471704e+04 6.471704e+04 6.380539e+04 6.380619e+04
2020 7.962083e+04 7.962083e+04 7.843322e+04 7.843424e+04
2021 9.607639e+04 9.607639e+04 9.452318e+04 9.452452e+04
2022 1.113036e+05 1.113036e+05 1.092868e+05 1.092888e+05
2023 1.265276e+05 1.265276e+05 1.239538e+05 1.239566e+05
2024 1.431621e+05 1.431621e+05 1.399327e+05 1.399371e+05
2025 1.612770e+05 1.612770e+05 1.572810e+05 1.572849e+05
2026 1.810041e+05 1.810041e+05 1.761030e+05 1.761110e+05
2027 2.024869e+05 2.024869e+05 1.965275e+05 1.965305e+05
2028 2.258817e+05 2.258817e+05 2.186523e+05 2.186624e+05
2029 2.513587e+05 2.513587e+05 2.426040e+05 2.426285e+05
2030 2.791031e+05 2.791031e+05 2.684088e+05 2.685572e+05
2031 3.093167e+05 3.093167e+05 2.964924e+05 2.965880e+05
2032 3.422194e+05 3.422194e+05 3.265795e+05 3.268766e+05
2033 3.780504e+05 3.780504e+05 3.594092e+05 3.595981e+05
2034 4.170703e+05 4.170703e+05 3.944386e+05 3.949450e+05
2035 4.595630e+05 4.595630e+05 4.321484e+05 4.331229e+05
2036 5.058376e+05 5.058376e+05 4.738051e+05 4.743466e+05
2037 5.562306e+05 5.562306e+05 5.168462e+05 5.188456e+05
2038 6.111086e+05 6.111086e+05 5.655083e+05 5.668911e+05
2039 6.708708e+05 6.708708e+05 6.179569e+05 6.188420e+05
2040 7.359517e+05 7.359517e+05 6.738941e+05 6.751908e+05
2041 8.068249e+05 8.068249e+05 7.358673e+05 7.365804e+05
2042 8.840058e+05 8.840058e+05 8.034410e+05 8.037732e+05
2043 9.680558e+05 9.680558e+05 8.770290e+05 8.775781e+05
2044 1.059586e+06 1.059586e+06 9.577546e+05 9.587624e+05
2045 1.159263e+06 1.159263e+06 1.047591e+06 1.047979e+06
2046 1.267811e+06 1.267811e+06 1.145374e+06 1.145737e+06
2047 1.386019e+06 1.386019e+06 1.252346e+06 1.252450e+06
2048 1.514749e+06 1.514749e+06 1.368511e+06 1.368533e+06
2049 1.654935e+06 1.654935e+06 1.494535e+06 1.494556e+06
2050 1.807597e+06 1.807597e+06 1.631358e+06 1.631361e+06
In [16]:
data
Out[16]:
newInstalledCapacity_standard_[MW] newInstalledCapacity_decadence_[MW]
year
1995 1.250000e+01 1.250000e+01
1996 2.216767e+01 2.216767e+01
1997 3.378967e+01 3.378967e+01
1998 4.557067e+01 4.557067e+01
1999 6.245533e+01 6.245533e+01
2000 8.338200e+01 8.338200e+01
2001 1.113670e+02 1.113670e+02
2002 1.538430e+02 1.538430e+02
2003 2.135880e+02 2.135880e+02
2004 3.083400e+02 3.083400e+02
2005 4.049212e+02 4.049212e+02
2006 5.031342e+02 5.031342e+02
2007 8.273670e+02 8.273670e+02
2008 1.141952e+03 1.141952e+03
2009 1.573197e+03 1.573197e+03
2010 2.293041e+03 2.293041e+03
2011 4.074418e+03 4.074418e+03
2012 7.138581e+03 7.138581e+03
2013 1.117603e+04 1.117603e+04
2014 1.489823e+04 1.489823e+04
2015 2.173256e+04 2.173256e+04
2016 3.463022e+04 3.463022e+04
2017 4.441473e+04 4.441473e+04
2018 5.376726e+04 5.376726e+04
2019 6.471704e+04 6.471704e+04
2020 7.962083e+04 7.962083e+04
2021 9.607639e+04 9.607639e+04
2022 1.113036e+05 1.113036e+05
2023 1.265276e+05 1.265276e+05
2024 1.431621e+05 1.431621e+05
2025 1.612770e+05 1.612770e+05
2026 1.810041e+05 1.810041e+05
2027 2.024869e+05 2.024869e+05
2028 2.258817e+05 2.258817e+05
2029 2.513587e+05 2.513587e+05
2030 2.791031e+05 2.791031e+05
2031 3.093167e+05 3.093167e+05
2032 3.422194e+05 3.422194e+05
2033 3.780504e+05 3.780504e+05
2034 4.170703e+05 4.170703e+05
2035 4.595630e+05 4.595630e+05
2036 5.058376e+05 5.058376e+05
2037 5.562306e+05 5.562306e+05
2038 6.111086e+05 6.111086e+05
2039 6.708708e+05 6.708708e+05
2040 7.359517e+05 7.359517e+05
2041 8.068249e+05 8.068249e+05
2042 8.840058e+05 8.840058e+05
2043 9.680558e+05 9.680558e+05
2044 1.059586e+06 1.059586e+06
2045 1.159263e+06 1.159263e+06
2046 1.267811e+06 1.267811e+06
2047 1.386019e+06 1.386019e+06
2048 1.514749e+06 1.514749e+06
2049 1.654935e+06 1.654935e+06
2050 1.807597e+06 1.807597e+06
In [17]:
        mylegend = [col.split('_')[1:] for col in data]
        mylegend = [col[:-1] for col in mylegend]
        mylegend
Out[17]:
[['standard'], ['decadence']]
In [18]:
test_list=['standard', 'decadence', '2standard', '2decadence']
test_list2=['asdfadsf', 'dfdffd', 'dfsfs', 'ssssssss']
#str(test_list)[1:-1]
test_list + test_list2
Out[18]:
['standard',
 'decadence',
 '2standard',
 '2decadence',
 'asdfadsf',
 'dfdffd',
 'dfsfs',
 'ssssssss']
In [19]:
test_list
Out[19]:
['standard', 'decadence', '2standard', '2decadence']
In [ ]: